hey_look_its_shiny t1_j5fu53q wrote
Reply to comment by [deleted] in [D] Couldn't devs of major GPTs have added an invisible but detectable watermark in the models? by scarynut
You don't need to implement a full-scale LLM in order to degrade watermarks at scale or even mix-and-match watermarked inputs. People who aren't even trying get halfway there now with crappy synonym engines.
And before you ask, no, I'm not going to technically spec it for you. Instead I suggest using the upvote pattern from this expert community to run backprop on your beliefs. ;)
[deleted] t1_j5fv06l wrote
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BitterAd9531 t1_j5g52os wrote
>Besides that, OP stated that he wants to use a llm for this, not me.
Actually I didn't. If you read my comment you'd understand I would need the LLM to demonstrate the model that does the actual combining (which obviously wouldn't be an LLM). Seeing as there are currently no models that have watermarking, I'd have to write one myself to test the actual model that does the combining to circumvent the watermark. Either you didn't understand this, or you're once again taking single sentences out of context and making semi-valid points that don't have any relevancy to the orignal discussion.
But honestly I feel like this is completely besides the point. I've given you a high-level explanation of how these watermarks can be defeated and you seem to be the only one who does not understand how they work.
[deleted] t1_j5g7rb3 wrote
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hey_look_its_shiny t1_j5htrp4 wrote
> Besides that, OP stated that he wants to use a llm for this, not me.
Actually, you introduced that concept first when you said:
> If u want some AI to alter the text for you, you again need a LLM.
OP had not mentioned applying an LLM to the case prior to that. It was explicit in their original comment, and implicit in all comments thereafter, that a watermark-free LLM was only one of the ways in which this problem could be tackled.
Meanwhile:
> Synonym engines wouldnt change an n-gram watermarks significantly enough as a synonym is the same type of word so there are token patterns persisting.
Right. Hence why I said they "get halfway there". Halfway is clearly not "all the way", and thus not "significantly enough".
And finally:
> Rules for r/MachineLearning > 1. Be nice: no offensive behavior, insults or attacks
In light of your recent description of an interlocutor's "limited capacity brain", you seem to be catastrophically failing at (1) understanding the problem space being discussed, (2) understanding the deficiencies in your own arguments, and (3) understanding basic norms and rules of interpersonal decency....
Just my two cents, but this forum probably isn't the right space for you until you level up a bit.
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